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Improving the Identification of Layers in 3D Images of Ancient Papyrus Using Artificial Neural Networks

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Improving the Identification of Layers in 3D Images of Ancie...

IEEE Winter Applications and Computer Vision Workshops (WACVW)

作者： Nicolas Klenert Finn Schwoerer Noushin Hajarolasvadi Siloé Bournez Tobias Arlt Heinz-Eberhard Mahnke Verena Lepper Daniel Baum Department of Visual and Data-Centric Computing Zuse Institute Berlin Berlin Germany Helmholtz-Zentrum Berlin für Materialien und Energie Berlin Germany Ägyptisches Museum und Papyrussammlung Staatliche Museen zu Berlin Berlin Germany

ISBN: (数字)9798331536626

ISBN: (纸本)9798331536633

The non-invasive digital unfolding of ancient documents, such as folded papyrus packages, from 3D image data aims to reveal previously hidden writing without risking to damage the precious documents. One of the main tasks in this process is the geometric reconstruction of the writing sub-strate, which is a prerequisite for its subsequent unfolding. All current reconstruction methods require the existence of an interspace between different layers of the document to ensure a correct topology. Layers that appear merged to-gether in the 3D image often result in wrong connections between layers and thus also in a wrong topology of the reconstructed geometry, which hinders the successful unfolding. Here, we propose to use a neural network to facilitate the discrimination of the layers. Using papyrus documents as an example of a particularly difficult writing material, we show that our approach significantly reduces the number of wrong connections and improves the overall identification of the layers. This in turn enables fully automatic digital unfolding of large areas of highly complex papyrus packages. Utilizing explainable AI (XAI) further allows us to explore the results of the applied neural network.

关键词： Geometry Computer vision Three-dimensional displays Network topology Explainable AI Conferences Writing Reconstruction algorithms Topology Image reconstruction

A Joint visual Compression and Perception Framework for Neuralmorphic Spiking Camera

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arXiv 2025年

作者： Feng, Kexiang Jia, Chuanmin Ma, Siwei Gao, Wen Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China National Engineering Research Center of Visual Technology Peking University Beijing100871 China Wangxuan Institute of Computer Technology Peking University Beijing100080 China

The advent of neuralmorphic spike cameras has garnered significant attention for their ability to capture continuous motion with unparalleled temporal resolution. However, this imaging attribute necessitates considerable resources for binary spike data storage and transmission. In light of compression and spike-driven intelligent applications, we present the notion of Spike Coding for Intelligence (SCI), wherein spike sequences are compressed and optimized for both bit-rate and task performance. Drawing inspiration from the mammalian vision system, we propose a dual-pathway architecture for separate processing of spatial semantics and motion information, which is then merged to produce features for compression. A refinement scheme is also introduced to ensure consistency between decoded features and motion vectors. We further propose a temporal regression approach that integrates various motion dynamics, capitalizing on the advancements in warping and deformation simultaneously. Comprehensive experiments demonstrate our scheme achieves state-of-the-art (SOTA) performance for spike compression and analysis. We achieve an average 17.25% BD-rate reduction compared to SOTA codecs and a 4.3% accuracy improvement over SpiReco for spike-based classification, with 88.26% complexity reduction and 42.41% inference time saving on the encoding side. Copyright © 2025, The Authors. All rights reserved.

关键词： Image compression

Remaining Useful Life Prediction of Degraded Systems Based on Multiple Performance Indicators Constructed by Multiple Features

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IEEE Sensors Journal 2025年

作者： Wu, Bin Shi, Hui Zeng, Jianchao Zhang, Xiaohong Wang, Zhijian Wang, Zuolu Taiyuan University of Science and Technology School of Electronic Information Engineering Taiyuan030024 China North University of China institute for Big Data and Visual Computing Taiyuan030051 China University of Huddersfield Centre for Efficiency and Performance Engineering HuddersfieldHD1 3DH United Kingdom

The health status assessment of industrial equipment is critical to fault prediction and health management technologies. To fully reflect system degradation and accurately predict the remaining useful life (RUL) of a system, the construction of multiple performance indicators is important. Thus, a new state-space model and RUL prediction method is proposed herein based on multiple indicators, considering the three correlation relationships among features and degradation indicators. First, according to the extracted and selected feature signals, various relationships such as the correlations among multiple features, multiple features and indicators, and multiple indicators are analyzed. Then an intuitive multi-indicator state-space model is established considering the three correlations and the identified degradation patterns. On this basis, three failure modes of the system are defined according to the multi-indicator complex correlation conditions, and the corresponding probability density function of the system RUL is calculated. Finally, the PHM2012 Challenge and XJTU-SY datasets are used as examples for experimental verification. Compared with other prediction methods, the proposed method can intuitively reflect the correlation between features and indicators, to solve the problem caused by the hidden random correlation. © 2001-2012 IEEE.

关键词： Prediction models

End-to-end Optimized Lossy Compression for Neural-morphic Spiking Camera Captured Data

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Feng, Kexiang Jia, Chuanmin Pan, Jingshan Ma, Siwei Gao, Wen Chinese Academy of Sciences Institute of Computing Technology Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Peking University National Engineering Research Center of Visual Technology Beijing100871 China Peking University Wangxuan Institute of Computer Technology Beijing100080 China Shandong Computer Science Center National Supercomputer in Jinan Jinan250014 China Peking University National Engineering Research Center of Visual Technology School of Computer Science Beijing100871 China Peng Cheng Laboratory Shenzhen518055 China

Recently, the bio-inspired spike camera with continuous motion recording capability has attracted tremendous attention due to its ultra high temporal resolution imaging characteristic. Such imaging feature results in huge data storage and transmission burden compared to that of traditional camera, raising severe challenge and imminent necessity in compression for spike camera captured content. Existing lossy data compression methods could not be applied for compressing spike streams efficiently due to integrate-and-fire characteristic and binarized data structure. Considering the imaging principle and information fidelity of spike cameras, we propose a novel Reconstruction-based Contextual Spike Compression (RCSC) framework, which contains scene reconstruction, contextual image compression and spike generation. To our knowledge, it is the first learning-based model for efficient and robust spike stream compression with informative fidelity. Extensive experimental results show that our model outperforms the state-of-the-art conventional codec VVC intra by 6.14% and surpasses the state-of-the-art learned codec by 2.53% in BD-rate reduction, establishing a strong baseline for spike compression. © 1991-2012 IEEE.

关键词： Image compression

An efficient end-to-end computational framework for the generation of ECG calibrated volumetric models of human atrial electrophysiology

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arXiv 2025年

作者： Zappon, Elena Azzolin, Luca Gsell, Matthias A.F. Thaler, Franz Prassl, Anton J. Arnold, Robert Gillette, Karli Kariman, Mohammadreza Manninger-Wünscher, Martin Scherr, Daniel Neic, Aurel Urschler, Martin Augustin, Christoph M. Vigmond, Edward J. Plank, Gernot Division of Biophysics Medical University of Graz Graz Austria BioTechMed-Graz Graz Austria NumeriCor Gmbh Graz Austria Institute of Visual Computing Graz University of Technology Graz Austria Institute for Medical Informatics Statistics and Documentation Medical University of Graz Graz Austria Scientific Computing and Imaging Institute University of Utah United States Department of Biomedical Engineering University of Utah United States Clinical Department of Cardiology Medical University of Graz Graz Austria University of Bordeaux CNRS Bordeaux France

Computational models of atrial electrophysiology (EP) are increasingly utilized for applications such as the development of advanced mapping systems, personalized clinical therapy planning, and the generation of virtual cohorts and digital twins. These models have the potential to establish robust causal links between simulated in silico behaviors and observed human atrial EP, enabling safer, cost-effective, and comprehensive exploration of atrial dynamics. However, current state-of-the-art approaches lack the fidelity and scalability required for regulatory-grade applications, particularly in creating high-quality virtual cohorts or patient-specific digital twins. Challenges include anatomically accurate model generation, calibration to sparse and uncertain clinical data, and computational efficiency within a streamlined workflow. This study addresses these limitations by introducing novel methodologies integrated into an automated end-to-end workflow for generating high-fidelity digital twin snapshots and virtual cohorts of atrial EP. These innovations include: (i) automated multi-scale generation of volumetric biatrial models with detailed anatomical structures and fiber architecture;(ii) a robust method for defining space-varying atrial parameter fields;(iii) a parametric approach for modeling inter-atrial conduction pathways;and (iv) an efficient forward EP model for high-fidelity electro-cardiogram (ECG) computation. We evaluated this workflow on a cohort of 50 atrial fibrillation patients, producing high-quality meshes suitable for reaction-eikonal and reaction-diffusion models and demonstrating the ability to simulate atrial ECGs under parametrically controlled conditions. These advancements represent a critical step toward scalable, precise, and clinically applicable digital twin models and virtual cohorts, enabling enhanced patient-specific predictions and therapeutic *** Codes 92C50, 92C55, 92C30, 35Q92 Copyright © 2025, The Authors. All rights r

关键词： Virtual addresses

A visual APPROACH FOR HEALTH INFORMATION EXPLORATION: ADAPTIVE LEVELS OF visual GRANULARITY AND INTERACTION ANALYSIS

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arXiv 2025年

作者： Lengauer, Stefan Shao, Lin Miri, Hossein Bedek, Michael Kupfer, Cordula Zangl, Maria Kubicek, Bettina Dienstbier, Barbara Jeitler, Klaus Krenn, Cornelia Semlitsch, Thomas Zipp, Carolin Albert, Dietrich Siebenhofer, Andrea Schreck, Tobias Institute of Visual Computing Graz University of Technology Austria Fraunhofer Austria Center for Data Driven Design Austria International School of Engineering Chulalongkorn University Thailand Institute of Psychology University of Graz Austria Institute of General Practice and Evidence-based Health Services Research Medical University of Graz Austria Institute for Medical Informatics Statistics and Documentation Medical University of Graz Austria Institute for General Practice Goethe University Frankfurt am Main Germany

The effective and targeted provision of health information to consumers, specifically tailored to their needs and preferences, is indispensable in healthcare. With access to appropriate health information and adequate understanding, consumers are more likely to make informed and healthy decisions, become more proficient in recognizing symptoms, and potentially experience improvements in the prevention or treatment of their medical conditions. Most of today’s health information, however, is provided in the form of static documents. In this paper, we present a novel and innovative visual health information system based on adaptive document visualizations. Depending on the user’s information needs and preferences, the system can display its content with document visualization techniques at different levels of detail, aggregation, and visual granularity. Users can navigate using content organization along sections or automatically computed topics, and choose abstractions from full texts to word clouds. Our first contribution is a formative user study which demonstrated that the implemented document visualizations offer several advantages over traditional forms of document exploration. Informed from that, we identified a number of crucial aspects for further system development. Our second contribution is the introduction of an interaction provenance visualization which allows users to inspect which content, in which representation, and in which order has been received. We show how this allows to analyze different document exploration and navigation patterns, useful for automatic adaptation and recommendation functions. We also define a baseline taxonomy for adapting the document presentations which can, in principle, be leveraged by the observed user patterns. The interaction provenance view, furthermore, allows users to reflect on their exploration and inform future usage of the system. Copyright © 2025, The Authors. All rights reserved.

关键词： Electronic health record

COph100: A comprehensive fundus image registration dataset from infants constituting the "RIDIRP" database

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arXiv 2025年

作者： Hu, Yan Gong, Mingdao Qiu, Zhongxi Liu, Jiabao Shen, Hongli Yuan, Mingzhen Zhang, Xiaoqing Li, Heng Lu, Hai Liu, Jiang Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Beijing Tongren Eye Center Beijing Tongren Hospital Capital Medical University Beijing Ophthalmology and Visual Sciences Key Laboratory Beijing China Center for High Performance Computing and Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

Retinal image registration is vital for diagnostic therapeutic applications within the field of ophthalmology. Existing public datasets, focusing on adult retinal pathologies with high-quality images, have limited number of image pairs and neglect clinical challenges. To address this gap, we introduce COph100, a novel and challenging dataset known as the Comprehensive Ophthalmology Retinal Image Registration dataset for infants with a wide range of image quality issues constituting the public "RIDIRP" database1. COph100 consists of 100 eyes, each with 2 to 9 examination sessions, amounting to a total of 491 image pairs carefully selected from the publicly available dataset. We manually labeled the corresponding ground truth image points and provided automatic vessel segmentation masks for each image. We have assessed COph100 in terms of image quality and registration outcomes using state-of-the-art algorithms. This resource enables a robust comparison of retinal registration methodologies and aids in the analysis of disease progression in infants, thereby deepening our understanding of pediatric ophthalmic conditions. Copyright © 2025, The Authors. All rights reserved.

关键词： Image registration

Human-like conceptual representations emerge from language prediction

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arXiv 2025年

作者： Xu, Ningyu Zhang, Qi Du, Chao Luo, Qiang Qiu, Xipeng Huang, Xuanjing Zhang, Menghan School of Computer Science Fudan University Shanghai China Institute of Modern Languages and Linguistics Fudan University Shanghai China Shanghai Key Laboratory of Intelligent Information Processing Shanghai China Research Institute of Intelligent Complex Systems Fudan University Shanghai China Shanghai Collaborative Innovation Center of Intelligent Visual Computing Shanghai China Ministry of Education Key Laboratory of Contemporary Anthropology Fudan University Shanghai China

People acquire concepts through rich physical and social experiences and use them to understand the world. In contrast, large language models (LLMs), trained exclusively through next-token prediction over language data, exhibit remarkably human-like behaviors. Are these models developing concepts akin to humans, and if so, how are such concepts represented and organized? To address these questions, we reframed the classic reverse dictionary task to simulate human concept inference in context and investigated the emergence of human-like conceptual representations within LLMs. Our results demonstrate that LLMs can flexibly derive concepts from linguistic descriptions in relation to contextual cues about other concepts. The derived representations converged towards a shared, context-independent structure that effectively predicted human behavior across key psychological phenomena, including computation of similarities, categories and semantic scales. Moreover, these representations aligned well with neural activity patterns in the human brain, even in response to visual rather than linguistic stimuli, providing evidence for biological plausibility. These findings establish that structured, human-like conceptual representations can naturally emerge from language prediction without real-world grounding. More broadly, our work positions LLMs as promising computational tools for understanding complex human cognition and paves the way for better alignment between artificial and human intelligence. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantics

The NeRF Signature: Codebook-Aided Watermarking for Neural Radiance Fields

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arXiv 2025年

作者： Luo, Ziyuan Rocha, Anderson Shi, Boxin Guo, Qing Li, Haoliang Wan, Renjie Department of Computer Science Hong Kong Baptist University Hong Kong Institute of Computing University of Campinas Brazil State Key Laboratory of Multimedia Information Processing and National Engineering Research Center of Visual Technology School of Computer Science Peking University Beijing100871 China A*STAR Singapore Department of Electrical Engineering City University of Hong Kong Hong Kong

Neural Radiance Fields (NeRF) have been gaining attention as a significant form of 3D content representation. With the proliferation of NeRF-based creations, the need for copyright protection has emerged as a critical issue. Although some approaches have been proposed to embed digital watermarks into NeRF, they often neglect essential model-level considerations and incur substantial time overheads, resulting in reduced imperceptibility and robustness, along with user inconvenience. In this paper, we extend the previous criteria for image watermarking to the model level and propose NeRF Signature, a novel watermarking method for NeRF. We employ a Codebook-aided Signature Embedding (CSE) that does not alter the model structure, thereby maintaining imperceptibility and enhancing robustness at the model level. Furthermore, after optimization, any desired signatures can be embedded through the CSE, and no fine-tuning is required when NeRF owners want to use new binary signatures. Then, we introduce a joint pose-patch encryption watermarking strategy to hide signatures into patches rendered from a specific viewpoint for higher robustness. In addition, we explore a Complexity-Aware Key Selection (CAKS) scheme to embed signatures in high visual complexity patches to enhance imperceptibility. The experimental results demonstrate that our method outperforms other baseline methods in terms of imperceptibility and robustness. The source code is available at: https://***/luo-ziyuan/NeRF_Signature. Copyright © 2025, The Authors. All rights reserved.

关键词： Image watermarking